ABSTRACT Emerging as a promising paradigm for improving energy efficiency in error‐tolerant applications including image processing, neural networks, and embedded vision systems is approximative computing. Most current approximative adder designs, however, either compromise output quality or show poor trade‐off between logic complexity and computational accuracy. In order to close this gap, this work suggests a family of new 1‐bit approximate full adder (AFA) designs optimized with basic AND‐OR gate logic. While keeping reasonable error margins for real‐time image processing, these approaches decrease device footprint and power consumption. Conventional and state‐of‐the‐art approximate adders were compared against the proposed AFAs—AFA1, AFA2, and AFA3—on metrics including logic use, propagation delay, power dissipation, and Peak Signal‐to‐Noise Ratio (PSNR) in picture enhancement tasks. On an Intel Cyclone IV EP4CE115 FPGA, the AFAs attained up to 45.3% decrease in LUT utilization, 29.9% reduced power consumption, and 34.1% speed improvement over traditional full adders. The best‐performing design (AFA3) in image addition studies produced a PSNR of 34.6 dB, therefore verifying good perceptual integrity appropriate for use in practical vision applications. This work provides a compact, energy‐efficient design framework for digital image processing systems, therefore advancing the state of approximative arithmetic. Strong prospects for deployment in low‐power, resource‐constrained environments including IoT edge devices, and FPGA‐based accelerators are the architectural simplicity and error‐resilient behavior of the suggested adders.